The Journal of Neuroscience, September 17, 2008 • 28(38):9495–9503 • 9495
Behavioral/Systems/Cognitive
Evaluating the Negative or Valuing the Positive? Neural
Mechanisms Supporting Feedback-Based Learning across
Development
Anna C. K. van Duijvenvoorde,1,2,3 Kiki Zanolie,1,3,4 Serge A. R. B. Rombouts,1,3,5 Maartje E. J. Raijmakers,2 and
Eveline A. Crone1,3
1Leiden University Institute for Psychological Research, Leiden University, 2333 AK Leiden, The Netherlands, 2Department of Developmental Psychology,
University of Amsterdam, 1018 WB Amsterdam, The Netherlands, 3Leiden Institute for Brain and Cognition, Leiden University, 2300 RC Leiden, The
Netherlands, 4Department of Psychology, Erasmus University Rotterdam, 3000 DR Rotterdam, The Netherlands, and 5Department of Radiology, Leiden
University Medical Center, 2333 ZA Leiden, The Netherlands
How children learn from positive and negative performance feedback lies at the foundation of successful learning and is therefore of great
importance for educational practice. In this study, we used functional magnetic resonance imaging (fMRI) to examine the neural
developmental changes related to feedback-based learning when performing a rule search and application task. Behavioral results from
three age groups (8 –9, 11–13, and 18 –25 years of age) demonstrated that, compared with adults, 8- to 9-year-old children performed
disproportionally more inaccurately after receiving negative feedback relative to positive feedback. Additionally, imaging data pointed
toward a qualitative difference in how children and adults use performance feedback. That is, dorsolateral prefrontal cortex and superior
parietal cortex were more active after negative feedback for adults, but after positive feedback for children (8 –9 years of age). For 11- to
13-year-olds, these regions did not show differential feedback sensitivity, suggesting that the transition occurs around this age. Presupplementary motor area/anterior cingulate cortex, in contrast, was more active after negative feedback in both 11- to 13-year-olds and
adults, but not 8- to 9-year-olds. Together, the current data show that cognitive control areas are differentially engaged during feedbackbased learning across development. Adults engage these regions after signals of response adjustment (i.e., negative feedback). Young
children engage these regions after signals of response continuation (i.e., positive feedback). The neural activation patterns found in 11to 13-year-olds indicate a transition around this age toward an increased influence of negative feedback on performance adjustment. This
is the first developmental fMRI study to compare qualitative changes in brain activation during feedback learning across distinct stages
of development.
Key words: development; fMRI; prefrontal cortex; cognitive control; learning; feedback
Introduction
One of the key foundations for successful learning, such as in
school situations, is feedback-based learning, which refers to our
ability to use performance feedback in subsequent behavior. Both
positive and negative feedback are important for improving performance, signaling either a continuation or an adjustment of the
current behavior. When presented with negative feedback, adults
adjust behavior more successfully than 8- to 11-year-old children
(Crone et al., 2004, 2008; Huizinga et al., 2006). In contrast,
positive feedback can function as a reinforcer to continue current
behavior. For example, developmental studies show that chilReceived Jan. 6, 2008; revised July 2, 2008; accepted Aug. 5, 2008.
This work was supported by Netherlands Organisation for Scientific Research-VIDI grants (S.A.R.B.R., E.A.C) and
by the European Commission Framework 6 Project 516542 (New and Emerging Science and Technologies) (M.E.J.R.).
We thank Dietsje Jolles and Wouter van den Bos for their help with data analysis.
Correspondence should be addressed to Dr. Eveline A. Crone, Department of Developmental Psychology, Leiden
University Institute for Psychological Research, Leiden University, Wassenaarseweg 52, 2333 AK Leiden, The Netherlands. E-mail: [email protected].
DOI:10.1523/JNEUROSCI.1485-08.2008
Copyright © 2008 Society for Neuroscience 0270-6474/08/289495-09$15.00/0
dren’s cognitive performance improves on a rule shift task when
positive stimuli are used (Qu and Zelazo, 2007). However, the
neural mechanisms that support these behavioral differences are
currently unknown.
Medial prefrontal cortex (PFC), including the anterior cingulate cortex (ACC) and supplementary motor area (SMA)/preSMA (Rushworth et al., 2004, 2007), and dorsolateral PFC
(DLPFC) are often found to be more active after the presentation
of negative performance feedback in adults (Mars et al., 2005;
Zanolie et al., 2008a). Pre-SMA/ACC is thought to signal response conflict and to predict activation in DLPFC, a region that
is considered to be important for subsequent performance adjustment (Kerns et al., 2004). Previous developmental functional
magnetic resonance imaging (fMRI) studies demonstrated that
pre-SMA/ACC and DLPFC undergo developmental changes
both functionally (Crone et al., 2006b) and structurally (Gogtay
et al., 2004).
In addition to medial PFC and DLPFC, superior and inferior
parietal cortex are also often implicated in cognitive and behavioral regulation, such as stimulus conflict detection (Liston et al.,
9496 • J. Neurosci., September 17, 2008 • 28(38):9495–9503
van Duijvenvoorde et al. • Brain Regions Supporting Age Changes in Feedback Learning
Figure 1. Task design: trials were paired into guess and repetition trials. A cue indicated the dimension to select (shape or color) and was followed by the stimulus presentation. A response was
followed by positive (⫹) or negative (x) feedback, after which the sequence repeated during the repetition trial. Trials were separated by intertrial intervals (not shown) of 2– 8 s, during which a
central fixation cross was shown.
2006), response selection (Bunge et al., 2002), and feedback processing (Crone et al., 2008). A previous developmental fMRI
study that focused on negative feedback processing demonstrated
developmental differences in engagement of not only DLPFC and
pre-SMA/ACC, but also superior parietal cortex (Crone et al.,
2008), which is consistent with previous reports demonstrating
structural changes in parietal cortex across development (Gogtay
et al., 2004).
Finally, the caudate, with its interconnections to the prefrontal cortex, has been implicated in the learning of stimulus–response associations and is found to be sensitive to positive rather
than to negative feedback (Toni et al., 2002; Seger and Cincotta,
2006; Cincotta and Seger, 2007). Structural imaging studies indicate that the caudate’s developmental trajectory is similar to that
of frontal and parietal regions (Giedd, 2008).
The goal of this study was to investigate the neural developmental changes related to the ability to use positive and negative
performance feedback for subsequent behavioral adjustment. We
developed a child-friendly task in which participants adjusted
performance based on feedback by responding to either the color
or shape of the stimuli. Despite the developmental changes in
feedback-based learning and cognitive control generally between
ages 8 and 12 (Cepeda et al., 2001; Crone et al., 2004), previous
fMRI studies have collapsed across 8- to 12-year-olds. We focused on the changes within this age range by collecting behavioral and fMRI measurements in three age groups: 8- to 9-yearolds, 11- to 13-year-olds, and 18- to 25-year-olds. Based on
previous research, we examined feedback sensitivity in DLPFC,
pre-SMA/ACC, parietal cortex, and caudate. We expected increased pre-SMA/ACC, DLPFC, and superior parietal cortex activation in 18- to 25-year-olds after negative feedback and caudate activation after positive feedback. This will be the first study
to examine how these regions contribute to feedback-based rule
learning after negative relative to positive feedback across
childhood.
Materials and Methods
Participants. Fifty-five healthy volunteers were included in the study: 18
adults (11 female, 7 male; ages 18 –25; M ⫽ 22.3), 19 early adolescents (8
female, 11 male; ages 11–13; M ⫽ 11.7), and 18 children (8 female, 10
male; ages 8 –9; M ⫽ 8.6). Participants were paid volunteers, recruited
through local advertisements or through a university course credits system. All participants were right handed and reported normal or
corrected-to-normal vision and an absence of neurological or psychiatric
impairments. For children aged 8 –9 and 11–13 years, the Child Behavior
Checklist was filled out by the parents to secure an absence of psychiatric
conditions. All participants had total scores below the clinical range.
Participants gave informed consent for the study; for minors, the primary caretaker gave informed consent for participation. All procedures
were approved by the Leiden University Department of Psychology and
the medical ethical committee of the Leiden University Medical Center.
Two early adolescents were excluded from further analyses because of
poor task performance in the experimental phase, in which performance
was ⬍55% correct for either the positive or negative feedback condition.
Three additional participants (one early adolescent and two children)
were excluded because of excessive head movement (in total ⬎3 mm of
translation in any direction). Therefore, our final sample consisted of 18
adults, 16 adolescents, and 16 children.
Standard intelligence scores were obtained from each participant using the Raven’s Progressive Matrices test (Raven, 1941). Estimated mean
intelligence quotients were 117 for 8- to 9-year-olds, 118 for 11- to 13year-olds, and 123 for 18- to 25-year-olds and did not differ significantly
between age groups ( p ⫽ 0.11). The results from the adults have been
reported previously to confirm the involvement of DLPFC and preSMA/ACC in feedback processing (Zanolie et al., 2008b). In this previous
study, we reported the effects of intradimensional and extradimensional
switches, but this was not the focus of the current study.
Task. The task was a rule selection and application task in which participants were instructed to use positive and negative feedback to select
and apply a simple rule (i.e., one-dimensional rule) for target stimulus
selection (Zanolie et al., 2008b). The relevant rule set was cued by a
centrally presented equals sign and could be based either on color (colored equals sign) or on shape (white equals sign) of the stimuli. Stimuli
consisted of different pairs of nameable pictures that all differed on two
dimensions: color and shape (e.g., a red fish and a green car). These
paired stimuli were presented on the left and right sides of the cue. Both
their positions and colors were interchangeable within each pair, such
that both values of each stimulus dimension were present.
Stimuli were always presented in pairs of trials, in which participants
had to select the correct picture (left or right located stimulus picture of
the pair) by means of a middle or index finger button press from the left
hand (Fig. 1). In the first trial (referred to below as the guess trial),
participants had been cued the rule set (color rules or shape rules), but
did not yet know the correct response [expected p (correct) ⫽ 0.5]. For
example, a participant cued to apply the “color” rule on the guess trial
might arbitrarily select the red item as being correct. Participants’ responses generated a visually presented positive (⫹) or negative (x) feedback stimulus in the center of the screen, which indicated a correct or
incorrect choice, respectively. Feedback was followed by the second trial
(referred to as the repetition trial), in which the rule set and the stimulus
pair were repeated, although the colors and/or shapes of the pictures
could be switched. Participants had to choose the correct item based on
feedback they had received on the guess trial. Thus, if participants cor-
van Duijvenvoorde et al. • Brain Regions Supporting Age Changes in Feedback Learning
rectly used feedback after the guess trials, they could perform at 100%
accuracy on repetition trials. For every consecutive guess trial, a different
stimulus pair was presented (with different shape and color values), and
the rule set (color rules, shape rules) was either repeated or changed.
Trials had the following structure: a centrally located cue was presented for 500 ms, followed by a combined stimulus presentation and
response window for 2 s, after which feedback was presented for 1 s.
Timing was identical for guess and repetition trials. Intertrial intervals
were jittered in ⬃25% of the trials. This jitter varied exponentially from
2, 4, 6, and 8 s, based on an optimalization program [optseq2; see http://
surfer.nmr.mgh.harvard.edu/optseq/, developed by Dale (1999)]. During intertrial intervals, a central fixation cross was shown.
Participants were familiarized with the scanner environment on the
day of the fMRI session through the use of a mock scanner, which simulated the sounds and environment of a real MRI scanner. Immediately
before scanning, children and adults practiced the behavioral task in a
quiet laboratory to ensure proficiency on the task. All participants were
informed that their task in the experiment would be to make simple
decisions about pairs of pictures by means of a button press, and they
were instructed to respond as accurately as possible. During training
outside the scanner, participants received 15 practice trials of each rule
type, first a color and then a shape block, followed by one mixed block of
30 trials. In the fMRI session, participants completed four experimental
blocks, all consisting of 100 trials each. Technical malfunctions or excessive head movement rendered one block of trials unusable for one participant in the 11- to 13-year-old and one in the 8- to 9-year-old age
group.
MRI procedure. fMRI data were acquired with a standard whole-head
coil using a 3-Tesla Philips Achieva scanner in the Leiden University
Medical Center. T2*-weighted echoplanar images (EPIs) were obtained
during four functional runs of 232 volumes each, of which the first two
were discarded to allow for equilibration of T1 saturation effects. Each
volume covered the whole brain (38 slices of thickness 2.75 mm; field of
view, 220 mm; 80 ⫻ 80 matrix; in-plane resolution, 2.75 mm) and was
acquired every 2200 ms (echo time ⫽ 30 ms, ascending interleaved acquisition). A high-resolution T1-weighted anatomical scan was obtained
from each participant after the functional runs. Visual stimuli were projected onto a screen located at the head of the scanner bore and viewed by
participants by means of a mirror mounted to the head coil assembly. In
accordance with Leiden University Medical Center policy, all anatomical
scans were reviewed and cleared by the radiology department after each
scan. No anomalous findings were reported.
fMRI data analysis. Data were preprocessed using SPM2 (Wellcome
Department of Cognitive Neurology). Images were corrected for differences in timing of slice acquisition, followed by rigid body motion correction. Functional volumes were spatially normalized to EPI templates.
The normalization algorithm used a 12-parameter affine transformation
together with a nonlinear transformation involving cosine basis functions and resampled the volumes to 3 mm cubic voxels. Templates were
based on the MNI305 stereotaxic space (Cocosco et al., 1997), an approximation of Talairach space (Talairach and Tourneaux, 1988). Functional
volumes were spatially smoothed with an 8 mm full-width at halfmaximum isotropic Gaussian kernel. Statistical analyses were performed
on individual subjects’ data using the general linear model in SPM2. The
fMRI time series data were modeled by a series of events convolved with
a canonical hemodynamic response function (HRF). The feedback stimulus of each trial (guess and repetition trials) was modeled as a zeroduration event. Trials on which participants did not respond within the
2 s time window or trials that were followed by an incorrect trial on the
repetition trial were modeled separately and were not included in the
pairwise contrasts of interest. The modeled events were used as covariates
in a general linear model, along with a basic set of cosine functions that
high-pass filtered the data, and a covariate for session effects. The leastsquares parameter estimates of height of the best-fitting canonical HRF
for each condition were used in pairwise contrasts. The resulting contrast
images, computed on a subject-by-subject basis, were submitted to
group analyses. At the group level, contrasts between conditions were
computed by performing one-tailed t tests on these images, treating subjects as a random effect. Task-related responses were considered signifi-
J. Neurosci., September 17, 2008 • 28(38):9495–9503 • 9497
Figure 2. A, B, Performance on repetition trials after receiving negative and positive feedback (FB) indicated by percentages correct (A) and reaction time (B). Results are presented
separately for rule type (shape and color), with SEs plotted.
cant if they consisted of at least 10 contiguous voxels that exceeded an
uncorrected threshold of p ⬍ 0.001. We performed region of interest
(ROI) analyses to further test for age ⫻ condition effects.
ROI analyses were performed with the Marsbar toolbox in SPM2
(Brett et al., 2002) (http://marsbar.sourceforge.net/). ROIs that spanned
several functional brain regions were subdivided by sequentially masking
the functional ROI with each of several anatomical Marsbar ROIs. The
contrast used to generate functional ROIs was based on the general contrast all feedback stimuli ⬎ fixation, p ⬍ 0.005 (uncorrected) across all
participants. For all ROI analyses, effects were considered significant at
an ␣ of 0.0125, based on Bonferroni correction for multiple comparisons
( p ⫽ 0.05/4 ROIs), unless reported otherwise. For each ROI, the center
of mass is reported.
Results
Performance
First, to investigate the general behavioral effect of feedback presentation between age groups, guess trial response accuracy (by
design of the task at 50% accuracy) was used to establish a performance baseline. The difference score in accuracy between
guess trials and repetition trials was analyzed with a between-agegroup (8 –9, 11–13, and 18 –25) ANOVA. Results showed that, as
expected, the amount of accuracy improvement was greater for
young adults (M ⫽ 43%) than for 11- to 13-year-olds (M ⫽ 35%)
and 8- to 9-year-olds (M ⫽ 20%) (main effect of age group, F(2,47)
⫽ 20.43; p ⬍ 0.001) (Fig. 2 A). Post hoc tests confirmed that
differences were significant for all age comparisons (all p values ⬍
0.05).
Second, to examine accuracy differences on repetition trials
after positive and negative feedback, an age group (8 –9, 11–13,
and 18 –25) ⫻ rule type (color and shape) ⫻ feedback (negative
and positive) ANOVA was performed. This analysis was performed on repetition trials only because accuracy for guess trials,
by design of the task, was at chance level for all age groups. As
expected, general accuracy on repetition trials was higher for
young adults (M ⫽ 93%) than for 11- to 13-year-olds (M ⫽ 85%)
and 8- to 9-year-olds (M ⫽ 70%) (main effect of age group, F(2,47)
⫽ 29.34; p ⬍ 0.001). Post hoc tests confirmed that differences
were significant for all age comparisons (all p values ⬍ 0.05).
Further, accuracy on repetition trials was higher after receiving positive feedback on the previous guess trial (M ⫽ 87%) than
9498 • J. Neurosci., September 17, 2008 • 28(38):9495–9503
after negative feedback (M ⫽ 79%) (main effect of feedback,
F(1,47) ⫽ 75.05; p ⬍ 0.001). The decrease in performance on repetition trials after receiving negative feedback was larger for children (age group ⫻ feedback interaction, F(2,47) ⫽ 3.37; p ⬍ 0.05)
(Fig. 2 A). Post hoc comparisons confirmed that the difference
between positive and negative feedback accuracy was larger for 8to 9-year-olds than for young adults, and 11- to 13-year-olds
performed at an intermediate level. Finally, sorting according to
shape (M ⫽ 81%) was slightly but significantly more effortful
than sorting according to color rules (M ⫽ 84%) (main effect of
rule type, F(1,47) ⫽ 20.01; p ⬍ 0.001). This effect did not differ
significantly between age groups ( p ⬎ 0.1).
By subdividing the task into four blocks of 50 trials (total 200
trials), we examined also whether there were differential learning
patterns during the task between age groups. The age group ⫻
feedback ⫻ task block ANOVA showed no difference in overall
accuracy between age groups across task blocks (age group ⫻ task
block interaction, p ⫽ 0.11), nor a difference in accuracy after
positive or negative feedback across task blocks (age group ⫻
feedback ⫻ task block interaction, p ⫽ 0.86). Thus, there were no
differential learning patterns across task blocks for positive relative to negative feedback learning between age groups.
Finally, an age group (8 –9, 11–13, and 18 –25) ⫻ rule type
(color and shape) ⫻ feedback (negative and positive) ANOVA
was performed to examine reaction time (RT) differences on
repetition trials, which showed comparable results with accuracy
analyses. Overall, mean RTs were faster for young adults (M ⫽
511; SD ⫽ 98) and 11- to 13-year-olds (M ⫽ 579; SD ⫽ 98) than
for 8- to 9-year-olds (M ⫽ 744; SD ⫽ 86) (main effect of age
group, F(2,47) ⫽ 29.25; p ⬍ 0.001). Furthermore, results showed
that participants were faster on repetition trials after receiving
positive feedback (M ⫽ 585; SD ⫽ 120) than after receiving negative feedback (M ⫽ 638; SD ⫽ 145) (main effect of feedback,
F(1,47) ⫽ 30.17; p ⬍ 0.001). Finally, sorting according to shape
resulted in slower RTs (M ⫽ 643; SD ⫽ 134) than sorting according to color rules (M ⫽ 580; SD ⫽ 128) (main effect of rule type,
F(1,47) ⫽ 62.36; p ⬍ 0.001) (Fig. 2 B).
To summarize, the behavioral results show that young adults
performed more accurately in general and showed a larger behavioral improvement after feedback presentation than 11- to 13year-olds, who in turn performed more accurately and showed a
larger behavioral improvement than 8- to 9-year-olds. Furthermore, these results indicate that positive and negative feedback
differentially influenced subsequent performance adjustment
across age groups. Negative feedback in general resulted in lower
performance accuracy and slower RTs on subsequent repetition
trials than positive feedback. However, this decrease in accuracy
after negative feedback was larger for the younger participants.
Thus, when feedback is negative, i.e., requires a behavioral adjustment, that adjustment is considered more effortful for the youngest age group. In addition, all participants were less accurate and
slower to apply the shape sorting than the color sorting rules.
Whole-brain comparisons
The brain analyses focused specifically on feedback after guess
trials, because this feedback was informative for performance on
the next trial. Only those trials on which participants performed
accurately on the repetition trials were included in the analysis.
The number of guess trials included was 140 for 8- to 9-year-olds,
160 for 11- to 13-year-olds, and 180 for 18- to 25-year-olds. For
each age group, the amount of guess trials included consisted of
half positive feedback, half negative feedback.
The regions that were active for the negative ⬎ positive and
van Duijvenvoorde et al. • Brain Regions Supporting Age Changes in Feedback Learning
positive ⬎ negative contrasts are presented in Figure 3 ( p ⬍
0.001, at least 10 contiguous voxels). The coordinates are reported in supplemental Table 1 (available at www.jneurosci.org
as supplemental material) for the comparison negative ⬎ positive
feedback and supplemental Table 2 (available at www.
jneurosci.org as supplemental material) for the comparison positive ⬎ negative feedback for each age group separately.
The comparison negative feedback ⬎ positive feedback for
young adults resulted in activation in pre-SMA/ACC [Brodmann
area (BA) 6], left DLPFC (BA 9), right anterior prefrontal cortex
(BA 10), and bilateral inferior and superior parietal cortex (BA
7/40). The positive feedback ⬎ negative feedback comparison
resulted in activation in medial frontal gyrus (BA 10/11), precuneus (BA 31), and parahippocampal gyrus (BA 35, 36).
The comparison negative ⬎ positive feedback for 11- to 13year-olds resulted in activation in left pre-SMA/ACC (BA 6) and
right superior lateral PFC (BA 8). The reversed positive ⬎ negative contrast resulted in activation in medial frontal cortex (BA
10).
The comparison negative ⬎ positive feedback for 8- to 9-yearolds did not show any significant activation. The reversed positive ⬎ negative contrast resulted in activation in regions that were
sensitive to the reversed contrast in the older age groups, including pre-SMA/ACC (BA 6), superior lateral PFC (BA 8), and left
superior parietal areas (BA 7).
Together, the whole-brain contrasts suggest that age groups
were differentially sensitive to positive and negative feedback that
was informative for subsequent performance continuation and
adjustment.
To test whether the youngest (8- to 9-year-olds) and oldest
(18- to 25-year-olds) age groups had statistically different brain
responses to positive and negative feedback, we performed two
sample t tests (age group ⫻ feedback) on whole-brain positive
versus negative feedback contrasts to compare the youngest age
group (8- to 9-year-olds) and young adults (18- to 25-year-olds)
( p ⬍ 0.001, uncorrected, at least 10 contiguous voxels). Activation maps and the table of activations are presented in the supplemental material.
The comparison negative feedback ⬎ positive feedback for
18- to 25-year-olds ⬎ 8- to 9-year-olds, and the positive feedback ⬎ negative feedback for 8- to 9-year-olds ⬎ 18- to 25-yearolds, resulted in activation in bilateral pre-SMA/ACC (BA 6),
right DLPFC (BA 9/46), bilateral anterior prefrontal cortex (BA
10), left inferior and superior parietal cortex (BA 7/40), caudate,
and claustrum. The comparison positive feedback ⬎ negative
feedback for 18- to 25-year-olds ⬎ 8- to 9-year-olds, and the
comparison negative feedback ⬎ positive feedback for 8- to
9-year-olds ⬎ 18- to 25-year-olds, did not show any significant
activation.
Additionally, we performed ROI analyses to examine the agerelated feedback effects for different brain areas in more detail
and to determine how the differences in feedback sensitivity between age groups were affected by rule types.
ROI analyses
We performed ROI analyses for four a priori defined regions,
which were selected based on the unbiased all feedback events ⬎
fixation contrast across participants: DLPFC (⫺44, 19, 40), preSMA/ACC (⫺3, 16, 51), superior parietal cortex (⫺25, ⫺67, 70),
and caudate (17, 6, 14). One additional ROI analysis was performed for anterior lateral PFC (⫺40, 51, 2), which was selected
empirically, based on its consistent activation patterns in the
whole-brain contrasts. The results of this region are discussed in
van Duijvenvoorde et al. • Brain Regions Supporting Age Changes in Feedback Learning
J. Neurosci., September 17, 2008 • 28(38):9495–9503 • 9499
Figure 3. Feedback-locked whole-brain contrasts are displayed for 18- to 25-year-olds, 11- to 13-year-olds, and 8- to 9-year-olds, showing effects of negative feedback (FB) ⬎ positive FB at p ⬍
0.001 (uncorrected) versus effects of positive FB ⬎ negative FB at p ⬍ 0.001 (uncorrected). Coordinates are reported in supplemental Tables 1 and 2 (available at www.jneurosci.org as
supplemental material).
the supplemental results (available at www.jneurosci.org as supplemental material).
We focused on left lateralized regions because these showed
the strongest effects (except for caudate), but we additionally
compared these effects with the right lateralized ROIs, because of
bilateral activation patterns seen in the whole-brain contrasts at
more lenient thresholds. As in the whole-brain comparisons, the
analyses focused only on feedback after guess trials and were only
included when subsequent repetition trials were performed correctly. The data are organized by feedback (positive and negative)
and rule type (color and shape). The age ⫻ feedback interactions
for pre-SMA/ACC, left DLPFC, left superior parietal cortex, and
right caudate are presented in Figure 4.
Pre-SMA/ACC
The age group ⫻ feedback ⫻ rule type ANOVA for pre-SMA/
ACC resulted in the expected age group ⫻ feedback interaction
(F(2,47) ⫽ 5.93; p ⬍ 0.005). Post hoc comparisons for each age
group demonstrated that activation in pre-SMA/ACC was larger
after negative feedback than after positive feedback for young
adults (F(1,17) ⫽ 12.44; p ⬍ 0.005) and for 11- to 13-year-olds
(F(1,15) ⫽ 15.32; p ⬍ 0.001). For the 8- to 9-year-olds, however,
pre-SMA/ACC did not show a differential activation pattern for
positive versus negative feedback ( p ⫽ 0.55) (Fig. 4).
DLPFC
The same ANOVA for left DLPFC also resulted in the expected
age group ⫻ feedback interaction (F(2,47) ⫽ 8.73; p ⬍ 0.001). Post
hoc comparisons for each age group demonstrated that activation
in left DLPFC was larger after negative feedback than after positive feedback for young adults (F(1,17) ⫽ 11.73; p ⬍ 0.005). In
contrast, 8- to 9-year-olds showed the reversed pattern, in which
activation was larger after positive performance feedback than
after negative performance feedback (F(1,15) ⫽ 4.48; p ⫽ 0.05)
(Fig. 4). Eleven- to thirteen-year-olds did not show a differential
activation pattern for positive versus negative feedback ( p ⫽
0.80).
A similar analysis for right DLPFC resulted in the same age
group ⫻ feedback interaction (F(2,47) ⫽ 7.53; p ⬍ 0.001). A comparison between left and right DLPFC did not result in a significant interaction with region ( p ⫽ 0.75).
Superior parietal cortex
The ANOVA for left superior parietal cortex also resulted in an
age group ⫻ feedback interaction (F(2,47) ⫽ 13.3; p ⬍ 0.001). Post
hoc comparisons for each age group demonstrated that activation
in left superior parietal cortex was larger after negative feedback
than after positive feedback for young adults (F(1,15) ⫽ 12.48; p ⬍
0.005). In contrast, 8- to 9-year-olds showed the reversed pattern,
in which activation was larger after positive performance feedback than after negative feedback (F(1,15) ⫽ 9.7; p ⬍ 0.005). The
11- to 13-year-olds did not show a differential activation pattern
for positive versus negative feedback ( p ⫽ 0.97) (Fig. 4).
A similar analysis for right superior parietal cortex resulted in
the same age group ⫻ feedback interaction (F(2,47) ⫽ 6.18; p ⬍
0.005) with similar results for post hoc comparisons. A comparison between left and right superior parietal cortex did not result
in a significant interaction with region for this effect ( p ⫽ 0.56).
Caudate
Finally, the same analysis for left caudate did not show a significant effect of feedback ( p ⫽ 0.11). In contrast, the ANOVA for
right caudate showed a main effect for feedback (F(1,47) ⫽ 5.11;
p ⫽ 0.03), where positive feedback resulted in higher activation
9500 • J. Neurosci., September 17, 2008 • 28(38):9495–9503
van Duijvenvoorde et al. • Brain Regions Supporting Age Changes in Feedback Learning
Three additional analyses were conducted to account for several challenges when comparing participants of different age
groups: (1) correction for possible carryover effects of previous
negative feedback trials, (2) correction for number of trials included in the analysis, and (3) correction for head movement
differences.
Carryover effects of previous trials
We conducted an additional analysis to test whether the observed
differences in brain activation were related to possible carryover
effects of previous feedback, given that 8- to 9-year-old children
received more negative feedback in general. We performed a sequential analysis, in which guess trials followed by negative feedback were recoded into two types: those for which the previous
repetition trial was correct and those for which the previous repetition trial was incorrect. Similar recoding was done for guess
trials followed by positive feedback. For these recoded variables,
the feedback on each trial was modeled as a zero-duration event
in the fMRI analyses. Comparisons were conducted for the
youngest age group separately, given that this was the age group
with the highest prevalence of incorrect repetition trials, and
across age groups. Results showed that the previous trial did not
modulate the feedback effects or the age ⫻ feedback interactions
in all previous reported ROIs (all p values including effects of
previous feedback were NS). Also, for the youngest age group
separately, no interactions of previous feedback ⫻ feedback (negative vs positive) were observed in the ROIs for pre-SMA/ACC,
DLPFC, superior parietal cortex, and caudate (all p values ⬎
0.10).
Figure 4. ROI activation values for pre-SMA/ACC, left DLPFC, left superior (Sup) parietal
cortex, and right caudate for each age group. For the first three regions, an age group (18 –25,
11–13, and 8 –9) ⫻ feedback (negative and positive) interaction was present (all p values ⬍
0.005) ( post hoc analyses indicating significant differences between positive and negative
feedback, *p values ⬍ 0.05; **p values ⬍ 0.005). SEs are plotted.
levels than negative feedback. This effect did not differ across age
groups ( p ⫽ 0.27) (Fig. 4).
To summarize, the ROI analyses resulted in three main effects:
(1) DLPFC and superior parietal cortex were more active after
negative than positive feedback in adults, but after positive than
negative feedback in 8- to 9-year-olds. Eleven- to thirteen-yearolds did not reveal a differential sensitivity toward negative and
positive feedback in these regions. (2) Pre-SMA/ACC was more
active after negative than positive feedback in adults and 11- to
13-year-olds, whereas 8- to 9-year-olds did not demonstrate
feedback sensitivity in this region. (3) The caudate was more
active after positive feedback than after negative feedback, and
this effect did not differ between age groups.
Matching for number of observations
We conducted a second additional analysis to test whether the
observed differences in brain activation were related to differences in the number of observations between age groups. We
equated the average number of correct trials included in the fMRI
analyses across age groups. For both adults and 11- to 13-yearolds, we selected a random subset of 70% of the correct trials,
making the average percentage of correct trials 71% for young
adults, 72% for 11- to 13-year-olds, and 70% for 8- to 9-yearolds. For this subset of trials, the feedback on all trials was modeled as a covariate of no interest in the fMRI analyses. All feedback ⫻ age group effects reported earlier remained significant for
pre-SMA/ACC (F(2,47) ⫽ 3.6; p ⬍ 0.05), bilateral DLPFC (left,
F(2,47) ⫽ 5.1, p ⬍ 0.02; right, F(2,47) ⫽ 5.2, p ⬍ 0.01), and bilateral
superior parietal cortex (left, F(2,47) ⫽ 11.6, p ⬍ 0.001; right,
F(2,47) ⫽ 5.86, p ⬍ 0.01). However, the previous feedback main
effect in right caudate was nonsignificant ( p ⫽ 0.11).
Controlling for head motion differences
To investigate possible head movement differences between age
groups, average amount of head movement for each participant
was submitted to an age group ANOVA, by averaging the absolute value of translation in x–y–z directions per participants per
trial. Results showed a significant effect of age group on average
head movement values (F(2,47) ⫽ 8.8; p ⬍ 0.01), in which the 8- to
9-year-olds moved significantly more than the adults ( p ⬍
0.001). Movement parameters from 11- to 13-year-olds did not
differ significantly from either adults or 8- to 9-year-olds ( p ⬎
0.1 and p ⬎ 0.05, respectively). To control for the head movement differences between age groups, realignment parameters
were included in the design matrix, and the same analyses were
performed as described above. Results from ROI analyses showed
that all feedback ⫻ age group effects reported earlier remained
significant for pre-SMA/ACC (F(2,47) ⫽ 4.4; p ⬍ 0.02), bilateral
van Duijvenvoorde et al. • Brain Regions Supporting Age Changes in Feedback Learning
DLPFC (left, F(2,47) ⫽ 7.2, p ⬍ 0.01; right, F(2,47) ⫽ 5.7, p ⬍ 0.01),
and bilateral superior parietal cortex (left, F(2,47) ⫽ 12.3, p ⬍
0.001; right, F(2,47) ⫽ 6.0, p ⬍ 0.02). The feedback main effect in
right caudate was at trend level ( p ⫽ 0.078).
Together, these additional analyses demonstrate that the age
group differences in activation patterns of pre-SMA/ACC, bilateral DLPFC, and bilateral superior parietal cortex could not be
accounted for by differences in negative or positive feedback presentation on repetition trials, by different numbers of correct
trials being included in the fMRI analyses, or by differences in
amount of head movement across age groups.
Correlations
Finally, we performed correlation analyses for activation in brain
areas and behavioral performance within and across age groups,
but no significant brain– behavior correlations were found.
Discussion
The goal of this study was to examine the neural developmental
changes related to the ability to use positive and negative performance feedback for subsequent behavioral adjustment in a rule
application task. Consistent with previous behavioral studies, all
participants performed more accurately and faster on trials that
followed positive relative to negative feedback, but 8- to 9-yearold children had more difficulty in learning from negative feedback than adults, resulting in lower accuracy on repetition trials
for negative relative to positive feedback trials (Crone et al., 2004;
Schmittmann et al., 2006). Brain imaging data yielded three main
results: (1) with age, there was a shift in recruitment of DLPFC
and superior parietal cortex after positive and negative performance feedback; (2) there were separable developmental trajectories for involvement of DLPFC/superior parietal cortex and
pre-SMA/ACC; and (3) the caudate was more active after positive
feedback, but this effect was the same for all age groups. The
discussion is organized along the lines of these findings.
Sensitivity to positive and negative feedback
across development
Our analysis of sensitivity to positive and negative feedback demonstrated important changes in neural recruitment between ages
8 –9, 11–13, and young adults. Whereas young adults showed
more activation in DLPFC and superior parietal cortex after negative performance feedback than after positive feedback, this difference was not seen for 11- to 13-year-olds and was reversed for
8- to 9-year-olds. The adult findings are consistent with previous
studies, demonstrating negative feedback-related sensitivity in
DLPFC for feedback that is important for subsequent behavioral
adjustment (Kerns et al., 2004; Zanolie et al., 2008a). This result
confirms its role in the implementation of goal-directed and controlled behavior (Miller and Cohen, 2001). Superior parietal cortex showed a pattern of activation that was similar to the activation pattern seen in DLPFC. Previous imaging research supports
these similarities and emphasizes the interplay between prefrontal and parietal cortices in the implementation of cognitive control (Bunge et al., 2002; Brass et al., 2005) and feedback processing (Crone et al., 2008).
Previous developmental studies have suggested immature activation patterns for children in DLPFC and superior parietal
cortex related to other cognitive functions, such as working
memory (Klingberg et al., 2002; Crone et al., 2006a). In these
studies, it was suggested that these regions are not yet accessible to
young children because of structural immaturity. The current
results indicate that immaturity may also be related to differential
J. Neurosci., September 17, 2008 • 28(38):9495–9503 • 9501
sensitivity to the informative value of feedback presentation, or to
strategy differences, because children aged 8 –9 already recruited
prefrontal and parietal regions involved in goal-directed behavior when they received positive feedback. Cognitive developmental studies show that the complexity of strategies for conditional
reasoning increases significantly between ages 8 and 13 and is
related specifically to working memory capacity (Barrouillet and
Lecas, 1999). Possibly, the processing of negative feedback required an additional inference (e.g., if not car, then chair on the
next trial), which was more demanding in terms of working
memory recruitment than processing positive feedback (e.g., if
car is correct, then car again on the next trial). Thus, children may
use working memory capacity more successfully after positive
feedback than after negative feedback.
Although 11- to 13-year-olds did not recruit DLPFC and superior parietal cortex differentially after positive and negative
feedback, they showed more activation in pre-SMA/ACC after
negative feedback. In adult studies, pre-SMA/ACC was shown to
be sensitive to a negative feedback signal, which indicates that
outcomes are worse than anticipated (Holroyd and Coles, 2002)
or which signals a potential bad outcome (Brown and Braver,
2005), especially in situations in which the number of response
alternatives is large (Walton et al., 2004). Children aged 11–13
showed a pattern of feedback sensitivity in pre-SMA/ACC similar
to that in adults, whereas children aged 8 –9 did not demonstrate
this sensitivity. This finding suggests that the sensitivity of preSMA/ACC to outcome signals that indicate behavioral adjustment (Ullsperger and von Cramon, 2004) develops between ages
8 –9 and 11–13 years.
Separate developmental trajectories for DLPFC, superior
parietal cortex, and pre-SMA/ACC
The current findings indicate that pre-SMA/ACC matures earlier
than DLPFC and superior parietal cortex, suggesting that these
regions may have separate contributions to feedback processing.
In particular, DLPFC and superior parietal cortex are thought to
be sensitive to the informative value of the feedback, because
these areas respond to negative feedback in adults, but to positive
feedback in 8- to 9-year-olds. Pre-SMA/ACC is thought to be
sensitive to conflict and the need for a behavioral change (in the
form of presentation of negative feedback) and exhibits a mature
pattern of feedback response by age 11–13. These findings are
consistent with previous studies, which have demonstrated developmental changes between 8 and 12 years in error-related
event-related potentials, which are thought to originate from preSMA/ACC (Miltner et al., 1997; Davies et al., 2004).
Together, these developmental trajectories suggest that children aged 8 –9 learn from the informative value of feedback, with
a focus toward valuing positive feedback. In contrast, 11- to 13year-olds learn from conflict signals that depend on a general
performance-monitoring system. Finally, adults learn from both
the informative value, with a focus toward evaluating negative
feedback, and conflict signals. Some caution in the interpretation
of these findings is warranted, because it depends on reverse inferences (Poldrack, 2006), but the findings highlight possible
strategy differences across development in feedback-based learning. Moreover, previous behavioral modeling studies support the
difference in informative value of negative feedback for adults
versus child populations. A recent model states that children’s
networks show a decreased influence of negative reinforcement
(e.g., negative feedback) relative to positive reinforcement compared with adult networks (Berkeljon and Raijmakers, 2007),
consistent with the neural results shown in the current study.
9502 • J. Neurosci., September 17, 2008 • 28(38):9495–9503
The results of this study fit well with previous developmental
studies, which have demonstrated differential recruitment of prefrontal cortex and parietal cortex during cognitive control tasks
(Klingberg et al., 2002; Crone et al., 2006a,b). Recently, researchers have shown that regions that are sensitive to reward and risks,
in particular the orbitofrontal cortex and nucleus accumbens, are
also sensitive to developmental change, in particular in adolescence (Ernst et al., 2005; Galvan et al., 2006). Interestingly, in
these studies researchers demonstrated an early sensitivity to reward and an adult sensitivity to control demands. In this study,
we did not observe developmental differences in emotion-related
brain areas, but this may be the result of this task being a cognitive
learning task, rather than an emotional risk-taking task. In future
studies, it will be interesting to examine the developmental differences in feedback learning in this context as well (Eshel et al.,
2007).
The current results show that the caudate, in contrast to preSMA/ACC, DLPFC, and superior parietal cortex, is sensitive to
positive performance feedback, and this pattern was found for all
age groups. These results are consistent with previous developmental imaging studies, which have reported early recruitment of
the caudate in attention switching but late development of cortical areas (Casey et al., 2004). Thus, despite reports showing that
the caudate has a slow structural developmental trajectory
(Giedd, 2008), this study and others (Casey et al., 2004) report
early functional involvement of the caudate. The results of the
current study possibly indicate that brain areas that are responsive to positive performance feedback in adults function at adult
level earlier than brain areas that are responsive to negative performance feedback in adults.
It should be noted that the caudate’s response to positive feedback did not survive the significance threshold when additional
analyses were performed that corrected for number of observations or movement differences. Apparently, its sensitivity to performance feedback is less robust than for the cortical areas. Indeed, a previous feedback-learning study failed to show a
consistent effect of caudate in feedback processing (Zanolie et al.,
2008b), and other studies suggest that subparts of the caudate are
differentially activated for different phases of learning (Cincotta
and Seger, 2007). Future studies are necessary to understand the
caudate’s role in feedback processing in more detail.
Two further caveats of this study should be noted. First, we did
not find brain– behavior correlations. The absence of these correlations is possibly attributable to qualitative differences between age groups, with a shift in learning from positive to learning from negative feedback. Thus, the relation with behavior is
most likely not linear. Second, in this study we directly compared
learning from positive feedback with learning from negative feedback. In future studies, it will be important to compare these
learning effects with a neutral baseline condition to investigate
whether age groups differ mostly in learning from positive relative to neutral signals, learning from negative relative to neutral
signals, or both.
Conclusion
The different neural sensitivity to the informative value of feedback across ages is striking. Whereas adults use control regions
after signals of response adjustment, children show activation in
most of these brain regions after signals of behavior continuation,
with a transition around age 12. In this study, the specific distinction between a narrow age range in development (8- and 12-yearolds) led to important insights in associated neural developmental changes. Additionally, even after age 12, important neural and
van Duijvenvoorde et al. • Brain Regions Supporting Age Changes in Feedback Learning
behavioral changes occur in the feedback-based learning task. To
our knowledge, this is the first feedback-learning study separating these age groups. This study also highlighted that different
neural changes may be the result of strategy differences rather
than a failure to recruit a certain brain area. These strategy differences, both between and within age groups, should be a hallmark
for future studies trying to relate cognitive neuroscience to educational practice.
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